Extracting Coupling Matrix From Lossy Filters With Uneven-Qs Using Differential Evolution Optimization Technique

Cao, Weihua; Liu, Can; Yuan, Yan; Wu, Min

doi:10.1002/mmce.21269

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…Te working process of this paper mainly includes data collection and kernel canonical correlation analysis, electromechanical characteristic modeling, and model parameter identifcation. First, collect the input and output data pairs (d, S) generated during the tuning process, where d denotes the screw tuning height and S denotes the corresponding output response; secondly, the coupling matrix is extracted from the scattering parameter (S-parameters) as mentioned in [20], and the characteristic parameters under diferent modes are fused through the kernel canonical correlation analysis technology. Build a data set for electromechanical characteristic modeling; fnally, the electromechanical relationship model of the cavity flter based on multioutput support vector regression is established according to the collected data sets of input and output relationships.…”

Section: Theoretical Synthesis Of Cavity Filtermentioning

confidence: 99%

Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR

Chen

2023

International Journal of Antennas and Propagation

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Aiming at the problems of poor data effectiveness, low modeling accuracy, and weak generalization in the tuning process of microwave cavity filters, a parametric model for coaxial cavity filter using kernel canonical correlation analysis (KCCA) and multioutput least squares support vector regression (MLSSVR) is proposed in this study. First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression algorithm is used for parametric modeling to solve the problems of low accuracy and poor prediction performance; third, the support vector of the parameter model is optimized by the differential evolution whale algorithm (DWA) to improve the convergence and generalization ability of the model in actual tuning. Finally, the tuning experiments of two cavity filters with different topologies are carried out. The experimental results show that the proposed method has an obvious improvement in generalization performance and prediction accuracy compared with the traditional methods.

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Section: Theoretical Synthesis Of Cavity Filtermentioning

confidence: 99%

Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR

Chen

2023

International Journal of Antennas and Propagation

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“…However, [3, 4] did not discuss the influence of non‐ideal factors such as the attenuation factor on the accuracy of parameter extraction. To improve the adaptive ability of the algorithm, a method based on the improved Cauchy's method and vector fitting was proposed in [5]. This method can extract the scattering parameter ( S ‐parameters) numerator and denominator polynomial coefficients of the lossy microwave filter under different port phases.…”

Section: Introductionmentioning

confidence: 99%

Self‐learning tuning of coaxial cavity filter by using the poles and residues of admittance function

Luo

Zhou

et al. 2021

IET Microwaves Antenna & Prop

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To solve the complexity and blindness of the tuning process in the manufacture of microwave cavity filters, this study proposes a self-learning tuning method based on the poles and residues of the admittance function. First, the improved Cauchy's method based on the differential evolution algorithm is used to extract the poles and residues of the admittance parameters (Y-parameters) in a non-ideal environment, and the effect of different port phase shifts and cavity losses on the accuracy of parameter extraction is overcome. Second, a parametric model based on the experience and data fusion via the fuzzy neural network method is established according to the collected non-linear relation data. Furthermore, problems such as poor data reliability, low modelling accuracy and weak generalisation ability are solved. On this basis, an adaptive optimisation tuning of microwave cavity filters using an implicit space-mapping algorithm is proposed and problems such as convergence difficulty and dependence on the initial value are solved. The results of the online simulation show that the proposed method has a high tuning accuracy and fast tuning ability.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Because of the limitations of the above methods, ref. [] proposed an improved Cauchy method that not only solved the extraction of M in the state of inconsistent port phase, but also perfectly handled the deviation caused by the loss of each resonator. The limitation of this method lies in the assumption that the forward ( S 12 ) and reverse transmission characteristics ( S 21 ) are equal in extracting parameters, when the actual filter detuning is large, there is no equality between S 12 and S 21 , and the Feldtkeller equation is also not suitable for the extraction of polynomial coefficients.…”

Section: Introductionmentioning

confidence: 99%

Tuning model for microwave filter by using improved back‐propagation neural network based on gauss kernel clustering

Cao

2019

Int J RF Microw Comput Aided Eng

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Given the difficulty of a single model in dealing with complex systems. In this study, we propose a tuning model that uses a probabilistic fusion of sub‐optimal back‐propagation neural network based on the Gauss kernel clustering. This study focused mainly three aspects of work compared with the traditional tuning model. First, the calculation of the coupling matrix of scattering parameters is achieved by solving polynomial coefficients after eliminating the inconsistent phase shift and resonant cavity loss. Second, the best clustering center and a number were obtained by mapping the scattered data to high‐dimensional space, and the prediction of multi‐output variables were realized by sub‐model probability fusion. Third, an improved shuffled frog leaping algorithm was introduced to optimize the initial weights of the back‐propagation neural network, and a differential operation significantly improved the diversity of the population and the searchability of the algorithm. Finally, the experiment of nine‐order cross‐coupled filters shows that the proposed method has a better capability to train the weights and thresholds, which improves the generalization performance of the system.

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Extracting Coupling Matrix From Lossy Filters With Uneven-Qs Using Differential Evolution Optimization Technique

Cited by 11 publications

References 16 publications

Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR

Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR

Self‐learning tuning of coaxial cavity filter by using the poles and residues of admittance function

Tuning model for microwave filter by using improved back‐propagation neural network based on gauss kernel clustering

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